load_and_test_qinchen.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "load and test qinchen.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "viBv6OSjYTbh"
      },
      "source": [
        "## Reference: https://github.com/WuJie1010/Facial-Expression-Recognition.Pytorch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExE2Q3oR3fzB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55d37d9e-7104-4c59-be0d-454cbbcc5b91"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "%cd \"drive/MyDrive/CSC420-Project-master\""
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n",
            "/content/drive/MyDrive/CSC420-Project-master\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ow8hiYtEVOBG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32520b5a-c87e-4d3f-eb43-1b55a1c590ff"
      },
      "source": [
        "# !python preprocess_fer2013.py"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(28709, 2304)\n",
            "(3589, 2304)\n",
            "(3589, 2304)\n",
            "Save data finish!!!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9jSr28I2He4w"
      },
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "import torch.backends.cudnn as cudnn\n",
        "import torchvision\n",
        "import transforms as transforms\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "\n",
        "from torch.autograd import Variable\n",
        "\n",
        "# import argparse\n",
        "import utils\n",
        "# dataclass\n",
        "from fer import FER2013\n",
        "# model declarations\n",
        "from models import *"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJm8FBG7FUEn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0eb68c8a-db2d-4548-dbde-79c0e2303f1f"
      },
      "source": [
        "class Opt():\n",
        "  def __init__(self):\n",
        "    self.model = \"VGG_ba_small\"\n",
        "    self.dataset = \"FER2013\"\n",
        "    self.bs = 128\n",
        "    self.lr = .01\n",
        "  \n",
        "  def __str__(self):\n",
        "    return f\"model = {self.model}\\ndataset = {self.dataset}\\nbatchsize = {self.bs}\\nlearning rate = {self.lr}\"\n",
        "\n",
        "opt = Opt()\n",
        "print(opt)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "model = VGG_ba_small\n",
            "dataset = FER2013\n",
            "batchsize = 128\n",
            "learning rate = 0.01\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQ5nGYL3JUGP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4f420d6-4d7d-4b93-e476-892b2acdbc28"
      },
      "source": [
        "# params\n",
        "use_cuda = torch.cuda.is_available()\n",
        "print(\"training on CPU\" if not use_cuda else \"GPU in place!\")\n",
        "\n",
        "best_validation_acc = 0  # best validation accuracy\n",
        "best_validation_acc_epoch = 0\n",
        "best_test_acc = 0  # best test accuracy\n",
        "best_test_acc_epoch = 0\n",
        "\n",
        "learning_rate_decay_start = 40  # 50\n",
        "learning_rate_decay_every = 5 # 5\n",
        "learning_rate_decay_rate = 0.9 # 0.9\n",
        "\n",
        "cut_size = 44\n",
        "total_epoch = 250\n",
        "\n",
        "path = os.path.join(opt.dataset + '_' + opt.model)\n",
        "print(f\"savepath is {path}\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GPU in place!\n",
            "savepath is FER2013_VGG_ba_small\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlvFzPJUJXBr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e80998a9-2ba8-46ff-c6f1-fc684a7ea9f2"
      },
      "source": [
        "# # Data\n",
        "# print('==> Preparing data..')\n",
        "# transform_train = transforms.Compose([\n",
        "#     transforms.RandomCrop(cut_size),\n",
        "#     transforms.RandomHorizontalFlip(),\n",
        "#     transforms.ToTensor(),\n",
        "# ])\n",
        "\n",
        "# transform_test = transforms.Compose([\n",
        "#     transforms.TenCrop(cut_size),\n",
        "#     transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),\n",
        "# ])\n",
        "\n",
        "# trainset = FER2013(split = 'Training', transform=transform_train)\n",
        "# trainloader = torch.utils.data.DataLoader(trainset, batch_size=opt.bs, shuffle=True, num_workers=1)\n",
        "# validationset = FER2013(split = 'validation', transform=transform_test)\n",
        "# validationloader = torch.utils.data.DataLoader(validationset, batch_size=opt.bs, shuffle=False, num_workers=1)\n",
        "# testset = FER2013(split = 'test', transform=transform_test)\n",
        "# testloader = torch.utils.data.DataLoader(testset, batch_size=opt.bs, shuffle=False, num_workers=1)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==> Preparing data..\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LNzxbqV-Jmt4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5737f839-3a0b-4728-e0f2-000e3c9d1c1a"
      },
      "source": [
        "# Model\n",
        "\n",
        "if opt.model  == 'Resnet18':\n",
        "    net = ResNet18()\n",
        "elif opt.model == 'Xception':\n",
        "    net = Xception(input_channel=3)\n",
        "elif opt.model.startswith('VGG_ba_small'):\n",
        "    net = VGG(opt.model)\n",
        "\n",
        "print(f'==> Building model {opt.model}..')\n",
        "\n",
        "if use_cuda:\n",
        "    net.cuda()\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "# SGD could be more generalizable but it is slower in convergence\n",
        "# weight decay option adds regularization. \n",
        "optimizer = optim.SGD(net.parameters(), lr=opt.lr, momentum=.9, weight_decay=5e-4)\n",
        "# optimizer = optim.Adam(net.parameters(), lr=opt.lr, weight_decay=5e-4)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==> Building model VGG_ba_small..\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "md5_LHpVJo7h"
      },
      "source": [
        "# # Training\n",
        "# def train(epoch):\n",
        "#     print('\\n---\\nEpoch: %d' % epoch)\n",
        "#     net.train()\n",
        "#     train_loss = 0\n",
        "#     correct = 0\n",
        "#     total = 0\n",
        "\n",
        "#     if epoch > learning_rate_decay_start and learning_rate_decay_start >= 0:\n",
        "#         frac = (epoch - learning_rate_decay_start) // learning_rate_decay_every\n",
        "#         decay_factor = learning_rate_decay_rate ** frac\n",
        "#         current_lr = opt.lr * decay_factor\n",
        "#         utils.set_lr(optimizer, current_lr)  # set the decayed rate\n",
        "#     else:\n",
        "#         current_lr = opt.lr\n",
        "#     print('learning_rate: %s' % str(current_lr))\n",
        "\n",
        "#     for batch_idx, (inputs, targets) in enumerate(trainloader):\n",
        "#         if use_cuda:\n",
        "#             inputs, targets = inputs.cuda(), targets.cuda()\n",
        "#         optimizer.zero_grad()\n",
        "#         inputs, targets = Variable(inputs), Variable(targets)\n",
        "#         outputs = net(inputs)\n",
        "#         loss = criterion(outputs, targets)\n",
        "#         loss.backward()\n",
        "#         utils.clip_gradient(optimizer, 0.1)\n",
        "#         optimizer.step()\n",
        "#         train_loss += loss.data\n",
        "#         _, predicted = torch.max(outputs.data, 1)\n",
        "#         total += targets.size(0)\n",
        "#         correct += predicted.eq(targets.data).cpu().sum()\n",
        "\n",
        "#         # for command line\n",
        "#         # utils.progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'\n",
        "#         #     % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))\n",
        "\n",
        "#     Train_acc = 100.*correct/total\n",
        "#     print(f\"Trainning Accuracy: {Train_acc:.10f}\")"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pt1yruFpJrnf"
      },
      "source": [
        "# def validation(epoch):\n",
        "#     global validation_acc\n",
        "#     global best_validation_acc\n",
        "#     global best_validation_acc_epoch\n",
        "#     net.eval()\n",
        "#     validation_loss = 0\n",
        "#     correct = 0\n",
        "#     total = 0\n",
        "#     for batch_idx, (inputs, targets) in enumerate(validationloader):\n",
        "#         bs, ncrops, c, h, w = np.shape(inputs)\n",
        "#         inputs = inputs.view(-1, c, h, w)\n",
        "#         if use_cuda:\n",
        "#             inputs, targets = inputs.cuda(), targets.cuda()\n",
        "#         with torch.no_grad():\n",
        "#           # inputs, targets = Variable(inputs, volatile=True), Variable(targets)\n",
        "#           outputs = net(inputs)\n",
        "#           outputs_avg = outputs.view(bs, ncrops, -1).mean(1)  # avg over crops\n",
        "#           loss = criterion(outputs_avg, targets)\n",
        "#           validation_loss += loss.data\n",
        "#           _, predicted = torch.max(outputs_avg.data, 1)\n",
        "#           total += targets.size(0)\n",
        "#           correct += predicted.eq(targets.data).cpu().sum()\n",
        "\n",
        "#           # for command line\n",
        "#           # utils.progress_bar(batch_idx, len(validationloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'\n",
        "#           #                   % (validation_loss / (batch_idx + 1), 100. * correct / total, correct, total))\n",
        "\n",
        "#     # Save checkpoint.\n",
        "#     validation_acc = 100.*correct/total\n",
        "#     print(\"validation_acc: %0.3f\" % validation_acc)\n",
        "#     if validation_acc > best_validation_acc:\n",
        "#         print('Saving..')\n",
        "#         print(\"best_validation_acc: %0.3f\" % validation_acc)\n",
        "        \n",
        "#         state = {\n",
        "#             'net': net.state_dict() if use_cuda else net,\n",
        "#             'acc': validation_acc,\n",
        "#             'epoch': epoch,\n",
        "#         }\n",
        "#         if not os.path.isdir(path):\n",
        "#             os.mkdir(path)\n",
        "#         torch.save(state, os.path.join(path,'validation_model.t7'))\n",
        "#         best_validation_acc = validation_acc\n",
        "#         best_validation_acc_epoch = epoch"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBFDoMKZJt9k"
      },
      "source": [
        "# # deprecated, we will be keeping test set only for the best validation model\n",
        "# def test(epoch):\n",
        "#     global test_acc\n",
        "#     global best_test_acc\n",
        "#     global best_test_acc_epoch\n",
        "#     net.eval()\n",
        "#     test_loss = 0\n",
        "#     correct = 0\n",
        "#     total = 0\n",
        "#     for batch_idx, (inputs, targets) in enumerate(testloader):\n",
        "#         bs, ncrops, c, h, w = np.shape(inputs)\n",
        "#         inputs = inputs.view(-1, c, h, w)\n",
        "#         if use_cuda:\n",
        "#             inputs, targets = inputs.cuda(), targets.cuda()\n",
        "#         with torch.no_grad():\n",
        "#           # inputs, targets = Variable(inputs, volatile=True), Variable(targets)\n",
        "#           outputs = net(inputs)\n",
        "#           outputs_avg = outputs.view(bs, ncrops, -1).mean(1)  # avg over crops\n",
        "#           loss = criterion(outputs_avg, targets)\n",
        "#           test_loss += loss.data\n",
        "#           _, predicted = torch.max(outputs_avg.data, 1)\n",
        "#           total += targets.size(0)\n",
        "#           correct += predicted.eq(targets.data).cpu().sum()\n",
        "\n",
        "#           utils.progress_bar(batch_idx, len(validationloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'\n",
        "#               % (test_loss / (batch_idx + 1), 100. * correct / total, correct, total))\n",
        "#     # Save checkpoint.\n",
        "#     test_acc = 100.*correct/total\n",
        "\n",
        "#     if test_acc > best_test_acc:\n",
        "#         print('Saving..')\n",
        "#         print(\"best_test_acc: %0.3f\" % max(test_acc, best_test_acc))\n",
        "#         state = {\n",
        "#             'net': net.state_dict() if use_cuda else net,\n",
        "#             'best_validation_acc': best_validation_acc,\n",
        "#             'best_test_acc': test_acc,\n",
        "#             'best_validation_acc_epoch': best_validation_acc_epoch,\n",
        "#             'best_test_acc_epoch': epoch,\n",
        "#         }\n",
        "#         if not os.path.isdir(path):\n",
        "#             os.mkdir(path)\n",
        "#         torch.save(state, os.path.join(path,'test_model.t7'))\n",
        "#         best_test_acc = test_acc\n",
        "#         best_test_acc_epoch = epoch"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xIVeKcM_Hall",
        "outputId": "379549db-5864-4944-8004-5a98a9a9e9b9"
      },
      "source": [
        "# print(opt)\n",
        "\n",
        "# # training loop\n",
        "# for epoch in range(total_epoch):\n",
        "#     train(epoch)\n",
        "#     validation(epoch)\n",
        "#     # test(epoch)\n",
        "\n",
        "# print(\"best_validation_acc: %0.3f\" % best_validation_acc)\n",
        "# print(\"best_validation_acc_epoch: %d\" % best_validation_acc_epoch)\n",
        "# # print(\"best_test_acc: %0.3f\" % best_test_acc)\n",
        "# # print(\"best_test_acc_epoch: %d\" % best_test_acc_epoch)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "model = VGG_ba_small\n",
            "dataset = FER2013\n",
            "batchsize = 128\n",
            "learning rate = 0.01\n",
            "\n",
            "---\n",
            "Epoch: 0\n",
            "learning_rate: 0.01\n",
            "Trainning Accuracy: 91.1421508789\n",
            "validation_acc: 63.416\n",
            "Saving..\n",
            "best_validation_acc: 63.416\n",
            "\n",
            "---\n",
            "Epoch: 1\n",
            "learning_rate: 0.01\n",
            "Trainning Accuracy: 87.7042007446\n",
            "validation_acc: 65.143\n",
            "Saving..\n",
            "best_validation_acc: 65.143\n",
            "\n",
            "---\n",
            "Epoch: 2\n",
            "learning_rate: 0.01\n",
            "Trainning Accuracy: 88.3346710205\n",
            "validation_acc: 64.113\n",
            "\n",
            "---\n",
            "Epoch: 3\n",
            "learning_rate: 0.01\n",
            "Trainning Accuracy: 89.4458160400\n",
            "validation_acc: 64.029\n",
            "\n",
            "---\n",
            "Epoch: 4\n",
            "learning_rate: 0.01\n",
            "Trainning Accuracy: 90.1076354980\n",
            "validation_acc: 64.726\n",
            "\n",
            "---\n",
            "Epoch: 5\n",
            "learning_rate: 0.01\n",
            "Trainning Accuracy: 90.7555084229\n",
            "validation_acc: 66.620\n",
            "Saving..\n",
            "best_validation_acc: 66.620\n",
            "\n",
            "---\n",
            "Epoch: 6\n",
            "learning_rate: 0.01\n",
            "Trainning Accuracy: 90.8704605103\n",
            "validation_acc: 66.397\n",
            "\n",
            "---\n",
            "Epoch: 7\n",
            "learning_rate: 0.01\n",
            "Trainning Accuracy: 91.0167541504\n",
            "validation_acc: 67.010\n",
            "Saving..\n",
            "best_validation_acc: 67.010\n",
            "\n",
            "---\n",
            "Epoch: 8\n",
            "learning_rate: 0.01\n",
            "Trainning Accuracy: 91.7447509766\n",
            "validation_acc: 66.592\n",
            "\n",
            "---\n",
            "Epoch: 9\n",
            "learning_rate: 0.01\n",
            "Trainning Accuracy: 91.6472167969\n",
            "validation_acc: 66.592\n",
            "\n",
            "---\n",
            "Epoch: 10\n",
            "learning_rate: 0.01\n",
            "Trainning Accuracy: 91.6924972534\n",
            "validation_acc: 64.558\n",
            "\n",
            "---\n",
            "Epoch: 11\n",
            "learning_rate: 0.01\n",
            "Trainning Accuracy: 92.2428512573\n",
            "validation_acc: 66.230\n",
            "\n",
            "---\n",
            "Epoch: 12\n",
            "learning_rate: 0.01\n",
            "Trainning Accuracy: 92.7130889893\n",
            "validation_acc: 66.760\n",
            "\n",
            "---\n",
            "Epoch: 13\n",
            "learning_rate: 0.01\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-60-8a4258791ce0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# training loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_epoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mvalidation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m# test(epoch)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-8-5b074a67fec2>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(epoch)\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/drive/MyDrive/CSC420-Project-master/models/vgg.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/batchnorm.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    109\u001b[0m             \u001b[0;31m# TODO: if statement only here to tell the jit to skip emitting this when it is None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_batches_tracked\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_batches_tracked\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_batches_tracked\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmomentum\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# use cumulative moving average\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m                     \u001b[0mexponential_average_factor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_batches_tracked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aeg9XVM9nrSr",
        "outputId": "11ba77f4-0ce5-4ccb-b1bc-44e8a1b38f65"
      },
      "source": [
        "net.load_state_dict(torch.load(os.path.join(path,'validation_model.t7'))['net'])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2EiDwe60DKpo"
      },
      "source": [
        "preprocess2 = transforms.Compose([\n",
        "    transforms.Resize(44),\n",
        "    transforms.CenterCrop(44),\n",
        "    transforms.ToTensor()\n",
        "])\n",
        "# load from whereever Qinchen's photos are stored\n",
        "my_face = torchvision.datasets.ImageFolder(root='../csc420_project/test_data/', transform=preprocess2)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PdSvhGZB9uz3"
      },
      "source": [
        "myfaceloader = torch.utils.data.DataLoader(my_face, batch_size=1,\n",
        "                                         shuffle=False, num_workers=2)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZM_Ax9CBdl7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "outputId": "71cf5510-5e7e-416f-986f-cbf448db8fed"
      },
      "source": [
        "plt.figure(figsize=(10, 15))\n",
        "device = torch.device(\"cuda\")\n",
        "\n",
        "for i, data in enumerate(myfaceloader, 0):\n",
        "  inputs, _ = data\n",
        "  inputs[0][1] = inputs[0][0]\n",
        "  inputs[0][2] = inputs[0][0]\n",
        "  outputs = net(inputs.cuda())\n",
        "  plt.subplot(3, 5, i+1)\n",
        "  plt.imshow(inputs[0][0], cmap='gray')\n",
        "  plt.title(outputs.argmax(axis=1).item())\n",
        "\n",
        "plt.show()\n",
        "  "
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x1080 with 14 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7jn4Yfzp2r-"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}